REDUCED-ORDER DYNAMIC MODELS FOR SEPARATION PROCESSES

One of the major difficulties with mathematical models of staged separation systems is the large dimensionality of the process model. This thesis is concerned with reduced-order steady state and dynamic models for processes such as distillation, absorption and extraction. The model reduction procedure is based on approximately the composition and flow profiles in the column using polynomials rather than as discrete functions of the stages. The number of equations required to describe the system is thus drastically reduced. The method is extended to accommodate multifeed separation systems. The reduced-order model is shown to conserve mass and energy balance in the steady state at all locations in the column.; The method is applied to the modelling and simulation of staged separation systems ranging from binary absorbers to complete distillation columns. The accuracy of the approximation is established by comparison of the steady state profiles and transient responses to that of rigorous dynamic models. The results indicate that the proposed technique is an effective way of reducing the number of equations needed to model stagewise multicomponent separation systems. The reduction achieved depends on the number of stages, number of components, the nonlinearity of the system, the nonideality of the vapor-liquid mixture and the accuracy desired.